Artificial Intuition for Automated Decision-Making

Figures & data

Figure 1. The overall architecture of this approach. All steps and phases are detailed in Sections 4, 5 and 6.

Figure 2. Overview of entrepreneur decision-making process.

Figure 3. The network and degree distribution of the network defined in Section 5.

Algorithm 1. Solution Assessment Algorithm

1: Let $t=1$ and $D\le 1$ be the threshold for the maximum entropy
2: Let ${\underset{\_}{x}}_t$ be a path connecting a query concept with one of its leaves leaf$(\underset{\_}{x})$
3: for path $\underset{\_}{x}$ do
4: if $\alpha ({\underset{\_}{x}}_t)\ge 1/(te)$ and $H({\underset{\_}{x}}_t)\le D$ then
5: Continue
6: else
7: Stop
8: end if
9: if ${\underset{\_}{x}}_t!=\mathrm{l}\mathrm{e}\mathrm{a}\mathrm{f}(\underset{\_}{x})$ then
10: $t=t+1$ Stop
11: else
12: stop
13: end if
14: end for
15: return path ${\underset{\_}{x}}_t,H({\underset{\_}{x}}_t)$

Table 1. Research Variables Detailed as per (Bolton and Cullen 2022), which were used to inform the creation, query and analysis of the data introduction and discussed in Sections 4, 6 and 5.

Variable	Unit	Value
Turnover	£	Numerical
Profit	£	Numerical
Productivity	£	Numerical
Increase in employment	Number of Employees	Numerical
Product and Service	New to market product
Development	or service developed	Binary
Technology Adoption	New to firm product and
	service developed	Binary
Reactive Strategic	Innovation initiated
Innovation	within the past 3 years	Binary
Ability to identify and
define problems	Manually assessed	Categorical
Ability to establish clear
objectives and priorities	Manually assessed	Categorical
Collecting data and evidence
evidence and identifying insights	Manually assessed	Categorical
Ability to evaluate options
for alternative solutions	Manually assessed	Categorical
Ability to develop optimal solutions
and action plans for implementation	Manually assessed	Categorical
Ability to monitor and evaluate
performance against objectives	Manually assessed	Categorical
Business Age	Years	Numerical
Size	Number of Employees	Numerical

Table 2. The experimental results as discussed in Section 7.

	Path length	Propagation Index	Innovation Index
Clear objectives	6	.71	0.51
and priorities
Data collection	4	.49	0.45
Technology adoption	9	.24	0.53
Business age	17	.82	0.81
Performance assessment	9	.51	0.34
Reactive strategic innovation	14	.27	0.56

Figure 4. The research motivation.

Bolton, S., and U. A. Cullen. 2022. Data driven assessment of inconsistent growth in SMEs. In Progress.

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